Corrigendum to “A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management” [Int. J. Proj. Manag. 36 (3) (April 2018) 397-413]

Abstract was revised as follows:In response to recent climate change, which is believed to be attributed to the release of greenhouse gas (GHG) emissions, many countries are placing CO₂ abatement programs such as carbon tax and cap-and-trade. Projects do have a significant share in GHGs and therefore their environmental performance, like their schedule and cost performance, should be monitored and controlled. Although many large projects would pass an environmental assessment in the project evaluation phase, the issue of environmental performance monitoring during the project execution phase has not been addressed in project management methodologies. The objective of this paper is to develop a model to estimate project GHG emissions, and to measure project GHG performance using the developed metrics, which can be used at any point in time over the life of a project. A comprehensive study is conducted to collect information on GHG emission factors of various project activity data (such as material use, energy and fuel consumption, transportation, etc.), and a user form interface is developed to calculate the total GHG of an activity. Also, a breakdown structure is proposed which supports managing all the project GHG accounts. The monitoring and control model is formulated based on the logic used in earned value management (EVM) methodology. The proposed model is then implemented to a work package of a real construction project. The results present the project initial GHG plan and show that the model is able to calculate project GHG variance by the reporting date and predict project final GHG based on a project GHG performance index. The method presented in this paper is general and can be applied to any type of projects in an organization that aims to reduce its carbon footprint. The same structure can be applied to monitor and control any other environmental impact associated with project execution process.     

A Catalyst‐Directed Remote Stereogenic Center Switch During the Site‐Selective Aldol Desymmetrization of Cyclohexanone‐Based Diketones

Site‐selectivity, differentiating members of the same functional group type on one substrate, is an emerging tactic for shortened advanced building block and biomolecule synthesis. Despite its potential, site‐selectivity remains less studied and especially so for ketone‐based substrates. During this work ketone site‐selectivity has been coupled with the chiral amine‐catalyzed aldol desymmetrization of 4‐keto‐substituted cyclohexanones, allowing three stereogenic centers to form in the aldol product while leaving the acyclic ketone unreacted. Unique here, compared to all previous 4‐substituted cyclohexanone desymmetrizations, is the first access to synthetically useful quantities of an epimeric (remote stereogenic center) aldol product. To demonstrate the value of these aldol products, we show their elaboration into eight keto‐acetonide and one keto‐lactone products. All compounds were isolated as single diastereomers and in high ee (≥96%). These efforts represent the first full characterization of aldol products with type III, Figure 2, relative stereochemistry, regardless of the enantiomer formed.

     

Optimizing a bi-objective multi-product EPQ model with defective items,rework and limited orders: NSGA-II and MOPSO algorithms

In this paper, a bi-objective multi-products economic production quantity (EPQ) model is developed, in which the number of orders is limited and imperfect items that are re-workable are produced. The objectives of the problem are minimization of the total inventory costs as well as minimizing the required warehouse space. The model is shown to be of a bi-objective nonlinear programming type, and in order to solve it two meta-heuristic algorithms namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm, are proposed. To verify the solution obtained and to evaluate the performance of proposed algorithms, two-sample t-tests are employed to compare the means of the first objective value, the means of the second objective values, and the mean required CPU time of solving the problem using two algorithms. The results show while both algorithms are efficient to solve the model and the solution qualities of the two algorithms do not differ significantly, the computational CPU time of MOPSO is considerably lower than that of NSGA-II.     

Maximum likelihood estimation from interval censored recurrent event data

In this paper, we consider the recurrent failures of several repairable units, which can only be observed at periodic inspection times. A unit is not aging over the period between a failure and its detection. The failure times are interval censored by the periodic assessment times. The observed data consists of censoring intervals of failure times and the unobserved data are the actual ages of the units at the failure times. We formulate the likelihood function and use several iterative algorithms to find the maximum likelihood estimate (MLE) of the parameters. The complete Expectation–Maximization (EM) algorithm, the EM gradient, full Newton–Raphson (NR), and the Simplex method are used. We derive recursive equations to calculate the expected values required in the algorithms. We estimate the parameters for four failure datasets, assuming that the failures follow a non-homogeneous Poisson process (NHPP). Three datasets are obtained from a hospital for the components of general infusion pump, and the fourth dataset is simulated. Since the estimation could take a long time, we compare the performance of the algorithms in terms of the required number of iterations to converge, the total execution time, and the precision of the estimated parameters. We also use Monte Carlo and Quasi-Monte Carlo simulation as the substitutes for the recursive procedures in the Expectation step of the EM gradient and compare the results.     

Modeling Failure Process and Quantifying the Effects of Multiple Types of Preventive Maintenance for a Repairable System

In this paper, we consider a repairable system whose failures follow a non‐homogenous Poisson process with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. A corrective maintenance has a minimal effect on the system; however, a preventive maintenance may reduce the system's age. We assume the effects of different preventive maintenance on the system are not identical and derive the likelihood function to estimate the parameters of the failure process as well as the effects of preventive maintenance. Moreover, we derive the conditional reliability and the expected number of failures between two consecutive preventive maintenance types. The proposed methods are applied to a real case study of four trucks used in a mining site in Canada. Copyright © 2016 John Wiley & Sons, Ltd.     

Toxicological Assessment of Inhaled Nanoparticles: Role of in Vivo,ex Vivo,in Vitro,and in Silico Studies

The alveolar epithelium of the lung is by far the most permeable epithelial barrier of the human body. The risk for adverse effects by inhaled nanoparticles (NPs) depends on their hazard (negative action on cells and organism) and on exposure (concentration in the inhaled air and pattern of deposition in the lung). With the development of advanced in vitro models, not only in vivo, but also cellular studies can be used for toxicological testing. Advanced in vitro studies use combinations of cells cultured in the air-liquid interface. These cultures are useful for particle uptake and mechanistic studies. Whole-body, nose-only, and lung-only exposures of animals could help to determine retention of NPs in the body. Both approaches also have their limitations; cellular studies cannot mimic the entire organism and data obtained by inhalation exposure of rodents have limitations due to differences in the respiratory system from that of humans. Simulation programs for lung deposition in humans could help to determine the relevance of the biological findings. Combination of biological data generated in different biological models and in silico modeling appears suitable for a realistic estimation of potential risks by inhalation exposure to NPs.     

Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes

Two types of Artificial Neural Networks (ANNs), a Multi-Layer Perceptron (MLP) and a Generalized Regression Neural Network (GRNN), have been used for the validation of a fluid bed granulation process. The training capacity and the accuracy of these two types of networks were compared. The variations of the ratio of binder solution to feed material, product bed temperature, atomizing air pressure, binder spray rate, air velocity and batch size were taken as input variables for training the MLP and GRNN. The properties of size, size distribution, flow rate, angle of repose and Hausner's ratio of granules produced, were measured and used as output variables. Qualitatively, the two networks gave comparable results, as both pointed out the importance of the binder spray rate and the atomizing air pressure to the granulation process. However, the averaged absolute error of the MLP was higher than the averaged absolute error of the GRNN. Furthermore, the correlation coefficients between the experimentally determined and the calculated output values, the corresponding prediction accuracy for the different granule properties as well as the overall prediction accuracy using GRNN were better than using MLP. In conclusion, the comparison of two different networks (MLP, a so-called feed-forward back-propagation network and GRNN, a so-called Bayesian Neural Network) showed the higher capacity of the latter for validation of such granulation processes.     

Fluidized-bed drying as a feasible method for dehydration of Enterococcus faecium M74

The suitability of fluidized-bed technology for the dehydration of probiotic Enterococcus faecium M74 was evaluated. Fluidized-bed drying was processed by layering the microorganisms on spherical pellets. The impact on cell viability of atomizing air pressure, processing temperature and time was investigated. Using 1.5 bar atomizing air pressure, 37 °C processing temperature, and 15 min processing time provided optimal dehydration condition. Changing these values resulted in excessive stress on the cells and affected the cell viability. Next, we compared the impact on cell viability of fluidized-bed drying with that of freeze-drying. Fluidized-bed drying caused more substantial losses of cell viability. However, viability of cells pre-treated with membrane protective agents such as sucrose or skim milk was less affected by fluidized-bed drying than by freeze-drying, resulted in a minor degree of membrane damage after 2 months storage. Comparison of the flow characteristics of freeze-dried cells and fluidized-bed dried cells layered on spherical pellets showed the superior flowability of the latter.